async-sync-advisor

Async patterns optimize I/O-bound workloads by handling concurrent requests efficiently, while blocking patterns like tokio::task::spawn_blocking or rayon suit CPU-intensive tasks. Use async for database queries and API calls, spawn_blocking for heavy computation, and rayon for data-parallel work to maximize Lambda throughput.

Use spawn_blocking to offload CPU-intensive work from async code without blocking the runtime. This pattern prevents latency spikes in mixed I/O and compute workloads by running blocking operations on a dedicated thread pool while keeping I/O tasks concurrent.

Yes. Mixed workloads benefit from hybrid patterns: handle I/O concurrently with async/await, offload CPU tasks via spawn_blocking or rayon for parallel computation, and coordinate both patterns safely. This reduces end-to-end latency compared to purely async or purely blocking approaches.

Async-only designs starve CPU-bound tasks and create request queues; blocking-only designs waste throughput on I/O waits. Correct pattern matching—async for I/O, blocking or rayon for compute—directly improves request latency and Lambda cost efficiency under mixed workloads.

Profile your handler: I/O-bound workloads spend time waiting on external services (databases, APIs); CPU-bound workloads spend time in computation or data processing. Identify which dominates, then apply async for I/O dominance, spawn_blocking or rayon for CPU dominance, or hybrid strategies for mixed patterns.

Yes. Rayon provides data-parallel iteration suitable for Lambda's multi-core environment. Wrap rayon work with spawn_blocking to avoid blocking the async runtime, allowing safe parallel computation within async handlers for algorithms like map-reduce or bulk data processing.